Identifying similar and related words is not only key in natural language understanding but also a suitable task for assessing the quality of computational resources that organise words and meanings of a language, compiled by different means. This paper, which aims to be a reference for those interested in computing word similarity in Portuguese, presents several approaches for this task and is motivated by the recent availability of state-of-the-art distributional models of Portuguese words,

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... ich add to several lexical knowledge bases (LKBs) for this language, available for a longer time. The previous resources were exploited to answer word similarity tests, which also became recently available for Portuguese. We conclude that there are several valid approaches for this task, but not one that outperforms all the others in every single test. Distributional models seem to capture relatedness better, while LKBs are better suited for computing genuine similarity, but, in general, better results are obtained when knowledge from different sources is combined. Information 2018, 9, 35 2 of 21 approaches to adopt in more complex tasks, such as semantic textual similarity (for Portuguese, see ASSIN [2]) or other tasks involved in a NLU pipeline (useful for e.g., conversational agents), or in a semantically-enriched search engine. Indirectly, the resources underlying each approach end up also being compared. For instance, results provide cues on the most suitable LKBs for computing word similarity, also a strong hint on the quality and coverage of these resources. Overall, this work involved different procedures for computing word similarity and several resources, namely: two procedures applied to open Portuguese wordnets, including one fuzzy wordnet; two different procedures applied to available semantic networks for Portuguese, or to networks that result from their combination; one procedure based on the co-occurrence of words in articles of the Portuguese Wikipedia; and one final procedure that computes similarity from several different models of word embeddings currently available for Portuguese. The work is especially directed to those users that are interested in computing the similarity of Portuguese words but do not have the conditions for creating new broad-coverage semantic models from scratch. In fact, it can also be seen as a survey of resources-semantic models and benchmarks-currently available for this purpose. The remainder of this paper starts with a brief overview on semantic similarity, variants, common approaches, and a focus on this topic for Portuguese. After that, the benchmarks used here are presented, followed by a description of the resources and approaches applied. Before concluding, the results obtained for each approach are reported and discussed, which includes a look at the state-of-the-art and the combination of different approaches towards better results. In general, the best results obtained are highly correlated with human judgements. Yet, depending on the nature of the dataset, both the best approach and underlying resource is different. An important conclusion is that the best results are obtained, first, with approaches that combine different resources of the same kind, then, by combining the former with models of a different kind. Related Work Semantic similarity measures the likeness of the meaning transmitted by two units, which can either be instances in an ontology or linguistic units, such as words or sentences. This involves comparing the features shared by each meaning, which sets their position in a taxonomy, and considers semantic relations such as synonymy, for identical meanings, or hypernymy, hyponymy and co-hyponymy, for meanings that share several features. Semantic relatedness goes beyond similarity and considers any other semantic relation that may connect meanings. For instance, the concepts of dog and cat are semantically similar, but they are not so similar to bone. On the other hand, dog is more related to bone than cat is, because dogs like and are often seen with bones. Word similarity tests are collections of word pairs with a similarity score based on human judgements. To answer such tests, humans would either look for the words in a dictionary or search for their occurrence in large corpora, possibly with the help of a search engine. This has a parallelism with the common approaches for determining word similarity automatically and unsupervisedly: (i) corpus-based approaches, also known as distributional, resort to a large corpus and analyse the distribution of words; (ii) knowledge-based approaches exploit the contents of a dictionary or lexical knowledge base (i.e., a machine-friendly representation of dictionary knowledge). It should be noted that the distinction of similarity and relatedness is not very clear for everyone. Therefore, whether the test scores reflect similarity or relatedness is also not always completely clear. Nevertheless, approaches for one are often applied to the other. Corpus-based approaches rely on the distributional hypothesis-words that occur in similar contexts tend to have similar meanings [3]-and often represent words in a vector space model [4] . Recent work uses neural networks to learn vectors from very large corpora, which are more accurate and computationally efficient at the same time. Sucessfull models of this kind include word2vec [1], GloVE [5], or fastText [6] . The similarity of two words may also be computed from their probability of occurrence in a corpus. Pointwise Mutual Information (PMI) [7] quantifies the discrepancy between the probability of two